code: def _high_level_step(self, action):
      logger.debug("High level agent sets goal")
      self.current_goal = action
      self.steps_remaining_at_level = 25
      self.num_high_level_steps += 1
      self.low_level_agent_id = "low_level_{}".format(
          self.num_high_level_steps
      )
      obs = {self.low_level_agent_id: [self.cur_obs, self.current_goal]}
      rew = {self.low_level_agent_id: 0}
      done = truncated = {"__all__": False}
      return obs, rew, done, truncated, {}
  
file_path: ray/rllib/examples/env/windy_maze_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete, Tuple
  import logging
  import random
  from ray.rllib.env import MultiAgentEnv
label_desc: Use image node_id 4 for calling the HierarchicalWindyMazeEnv obj's underlying member method code with example usage: obj._high_level_step(action) and returns: obs,  rew,  done,  truncated,  dict